08/12/2026
By Marley O'Neil

The Francis College of Engineering's Department of Plastics Engineering invites you to attend a doctoral dissertation proposal defense by Venu Pillai.

Event Details

  • Candidate: Venu Pillai
  • Defense title: “A Unified Framework for Injection Molded Product Development: Bridging Engineering, Digital Engineering, and Intelligent Engineering”
  • Date: Thursday, Aug. 27, 2026
  • Time: 4:30 – 6 p.m.
  • Location: Emerging Technologies and Innovation Center (ETIC). Room 445

Committee

  • Advisor: Davide Masato, Associate Professor, Plastics Engineering, UML
  • Stephen Johnston, Professor, Plastics Engineering, UML
  • Amir Ameli, Associate Professor, Plastics Engineering, UML
  • Aravind Rammohan, Technology Manager, Corning Inc.

Abstract

Injection molding remains one of the most widely used manufacturing processes for polymer products; however, increasing demands for cost reduction, product quality, sustainability, and accelerated development cycles continue to challenge conventional engineering practices. This research first investigates the critical role of cooling system design in injection molding performance through a comprehensive review of numerical modeling, cooling analysis, and optimization methodologies. Particular emphasis is placed on straight-line, conformal, and hybrid cooling channel designs and their influence on cycle time, dimensional stability, warpage, and overall molded part quality. A complementary industrial case study examines the relationship between mold design, cooling efficiency, and product outcomes across parts of varying geometric complexity and multiple mold suppliers. The findings highlight the significant impact of cooling system design on mold lead time, process predictability, and manufacturing efficiency, while identifying opportunities for automation-driven design approaches that integrate simulation and optimization tools directly into the mold development process. Building upon this foundation, the research advances toward a Digital Engineering framework that integrates simulation-driven Process Integration and Design Optimization (PIDO) techniques to automate product development and decision-making. A multi-physics workflow combining parametric CAD modeling, injection molding simulation, structural analysis, and adaptive optimization was developed using SOLIDWORKS®, SIGMASOFT™, Ansys Mechanical™, and Ansys optiSLang™. Using an injection-molded pipette tip rack insert as a case study, eight geometric design variables and five molding process parameters were simultaneously explored within a 13-dimensional design space. Automated design exploration, surrogate modeling, and optimization enabled rapid evaluation of design alternatives while significantly reducing reliance on physical prototyping and traditional experimental approaches. The optimized solution achieved approximately 27% mass reduction while satisfying structural, dimensional, manufacturability, and cycle-time requirements, demonstrating substantial improvements in engineering efficiency, sustainability, and product performance. The work illustrates how digital engineering methodologies enable repeatable, knowledge-driven workflows that democratize access to advanced Computer-Aided Engineering (CAE) capabilities and accelerate innovation in plastic product development. The final phase of this research extends digital engineering into the emerging domain of Intelligent Engineering through the development of AI-assisted methodologies for design and manufacturing optimization. While conventional simulation-driven workflows rely heavily on expert interpretation and sequential decision-making, the proposed framework seeks to embed engineering knowledge, machine learning, and optimization intelligence into autonomous decision-support systems capable of learning from historical simulation, manufacturing, and product-performance data. By combining physics-based models with AI-driven prediction, recommendation, and optimization techniques, the research aims to create adaptive engineering workflows that improve design quality, reduce development effort, increase robustness of manufacturing processes, and enable faster convergence toward optimal solutions. Collectively, the three studies establish a progressive roadmap from engineering fundamentals and mold cooling optimization, through digital engineering and automated multi-physics design optimization, to intelligent AI-enabled engineering systems. The research contributes novel approaches for improving efficiency, sustainability, and competitiveness in injection-molded product development while advancing the broader transformation toward autonomous and data-driven engineering environments.